台灣的森林占了本島一半以上的土地面積，其中蘊藏了豐富的生物資源，為了能有效的進行林分結構的量測來掌控森林生態系的變化，利用航遙測技術來獲取大範圍的森林資訊為目前的主要的趨勢。空載光達系統具有直接獲取高精度三維坐標的點雲以及部分穿透的特性，應用在森林中可達到快速量測林分結構的三維空間資訊，能得到在垂直方向的冠層高度以及樹冠密度變化，因此可用來估計樹冠高度模型(Canopy Height Model, CHM)和葉面積指數(Leaf Area Index, LAI)。因此，本研究主要目的是探討如何應用空載光達資料估計森林地區之樹冠高度模型和葉面積指數。
應用空載光達資料產製數值高程模型(Digital Elevation Model, DEM)及數值表面模型(Digital Surface Model, DSM)是目前成熟的空載光達資料處理程序，將DSM減DEM即可獲得CHM。而LAI的估計則透過雷射光(對樹冠層的)穿透率(LPI)之估算來推估，本研究採用五種LPI計算方法來估計LAI，並將成果與現地觀測得的資料進行迴歸分析，以驗證實驗成果。實驗區位在台灣東南部森林區範圍，採用三種全波形空載光達系統所得到的多重回波點雲和全波形點雲，共六種不同組合的點雲資料，分別進行樹冠高度模型和葉面積指數的估計，並進行成果之比較。其中，全波形點雲是利用小波為基礎的萃取方式，從光達儀器記錄的連續反射波形資訊中獲取而得，主要是希望透過波形中隱含的物理特性，萃取出較完整的點雲來代表森林結構。
經由研究成果發現，利用六種點雲資料在製作森林樹冠高度模型中最大的標準偏差約在1.5公尺左右，主要是在森林表面高度落差較大的地方有明顯的差異。而在葉面積指數估計方面，是利用離散的點雲計算出五種穿透率指標 : (1)地面點占全部點的比例; (2)地面點強度值和全部點強度值的比例；(3)地面點與所有雷射光束的比值；(4)改良自(1)的穿透率指標，增加對單一回波地面點的加權；(5)以全波形資料計算的雷射光照射地物間面積與非地面的面積比。再將各種穿透率指標，與使用LAI-2000於現地測得的葉面積指數進行迴歸比較。實驗成果顯示，使用經改良後的第四種穿透率指標對於葉面積指數有最佳的解釋能力。而相較於即時解算的多重回波點雲，使用全波形的光達點雲穿透率指標，可提升對LAI的估計，R2達到0.8以上。

Efficiently obtaining the information in forest region such as forest structure, forest ecosystems is important for forestry management. Remote sensing has been considered as a practical technology to acquire the data of a large area. Compared with spectral images, airborne light detection and ranging (LiDAR) can provide three dimensional coordinates directly, and the penetration characteristics of LiDAR system makes the possibility of seeing through the canopy. Therefore, the structure or the terrain under the canopy can be characterized by the LiDAR point cloud data.
The purpose of this study is to estimate the Canopy Height Model (CHM) and the Leave Area Index (LAI) of a dense forest area by using airborne LiDAR data. CHM is estimated by taking the difference of DSM and DEM derived from LiDAR data. Estimation of LAI is achieved based on the calculation of Laser Penetration Index (LPI). Five calculations of LPI were applied in this paper: (1.) The ratio between the number of ground points and that of all the points; (2.) the ratio between the intensities of ground points and that of all the points; (3) the ratio between the number of ground points and the number of laser beams; (4) a weighting method modified from index (1); and (5) the ratio between the area of ground points and that of all the points.
The study area is in a nature broadleaf forest of south Taiwan. In this study, we use three sets of airborne LiDAR data acquired with different full waveform LiDAR systems including Leica ALS60, Riegl LMS-Q680i and Optech Pegasus HD400. All of these LiDAR systems are capable of recording full waveform data, then we can get the waveform point clouds by the echo detector to do the comparison. Our experiments results show that the accuracy of CHM by different LiDAR data is about 1.5 meter. And the fourth LPI index has the highest coefficient of determination (about 0.8) and the estimation of LAI can be improved by using the waveform points.

林務局，1995。第三次台灣森林資源及土地利用調查，臺灣省農林廳. 林務局
吳守從，陳永寬，2004。應用數位航測技術探討南仁山生態保護區林分高生長量，航測及遙測學刊 第九卷 第四期 第 21-34頁
浦瑞良、宮鵬(2002)，高光譜遙測及其應用，載於葉面積指數訊息的擷取(pp.158-159)台北:五南圖書出版股份有限公司
黃清美，2007。空載光達點雲穿透率探討，國立交通大學土木工程學系碩士學位論文。
彭炳勳，2007。應用空載光達資料推測林木樹高與葉面積指數，國立屏東科技大學森林系碩士學位論文。
劉棠瑞、蘇鴻傑，1983。森林植物生態學，台北市:台灣商務印書館股份有限公司。
蕭淳伊，2008。應用空載光達資料與遙測影像推估樹林分布及體積，國立成功大學測量及空間資訊學系碩士學位論文。
Ackermann, F., (1999) “Airborne laser scanning – present status and future expectations”, ISPRS Journal of Photogrammetry & Remote Sensing, 54 64-67
Anon. (1992) LAI-2000 Plant canopy analyzer. Instruction Manual. LI-Cor:90 p.
Arnaud, Jacob, Yi-Hsing Tseng, Chi-Kuei Wang (2011), “Investigating the penetration capability of airborne LiDAR for high vegetation of subtropical evergreen forest.” The 30th conferencn on Surveying and Geomatics, Taiwan.
Danson, FM, Morsdorf, F and Koetz, B (2009), “Airborne and terrestrial laser scanning for measuring vegetation canopy structure”, in: Laser Scanning for the Environmental Sciences , Wiley-Blackwell.
Duursma R.A., J.D. Marshall, A.P. Robinson (2003)” Leaf area index inferred from solar beam transmission in mixed conifer forests on complex terrain” Agricultural and Forest Meteorology 118:221–236
Hopkinson Chris, Laura Chasmer (2009), “Testing LiDAR models of fractional cover across multiple forest ecozones” Remote Sensing of Environment 113, Pages 275–288
Hyyppä, J., H. Hyyppä, D.Leckie , F.Gougeon, Yu X., Maltamo M. (2008). “Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests.”International Journal of Remote Sensing, 29(5): 1339-1366.
Jensen Jennifer L.R., Karen S. Humes, Lee A. Vierling, Andrew T. Hudak (2008)“Discrete return lidar-based prediction of leaf area index in two conifer forests” Remote Sensing of Environment 112:3947–3957
Jonckheere Inge, Stefan Fleck, Kris Nackaerts, Bart Muys, Pol Coppin, Marie Weiss, Frédéric Baret (2004)“Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography” Agricultural and Forest Meteorology 121, 19–35
Lillesand, Thomas M., Kiefer, (1987) “Concepts and foundations of remote sensing”, John Wiley & Sons, Inc. Chapter 1 in Remote sensing and image interpretation
Koetz B., F. Morsdorf, G. Sun, K. J. Ranson,K. Itten, B. Allgöwer (2006) ” Inversion of a Lidar Waveform Model for Forest Biophysical Parameter Estimation IEEE Geoscience And Remote Sensing Letters, Vol. 3, No. 1 P.49-P53
Masona Euan G.,, Mathijs Diepstratenb, Guy L. Pinjuvc, Jean-Pierre Lasserred (2012) “Comparison of direct and indirect leaf area index measurements of Pinus radiata D. Don” Agricultural and Forest Meteorology 166– 167 113– 119
Morsdorf F., B. Kötz , E. Meier, K.I. Itten , B. Allgöwer (2006)” Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction”, Remote Sensing of Environment 104 50–61
Morsdorf F., O. Frey, E. Meier, K.I. Itten , B. Allgöwer (2008) “Assessment of the influence of flying altitude and scan angle on biophysical vegetation products derived from airborne laser scanning” International Journal of Remote SensingVolume 29, Issue 5, pages 1387-1406
Nelson, R., Krabill, W. and Maclean, G. (1984), “Determining forest canopy characteristics using airborne laser data.” Remote Sensing of Environment, 15, pp. 201–212.
Næsset E.(1997), “Estimating timber volume of forest stands using airborne laser scanner data“ Remote Sensing of Environment Volume 61(2), Pages 246–253
Reitberger J., P. Krzystek, U. Stilla (2008) “Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees” International Journal of Remote Sensing Vol. 29, No. 5, 1407–1431
Solberg, S., Næsset, E., Hanssen, K.H. and Christiansen, E. (2006), “Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning.” Remote Sensing of Environment, 102, pp. 364–376.
Solberg, S. (2008),”Comparing discrete echoes counts and intensity sums from ALS for estimatingforest LAI and gap fraction.” In Proceedings of SilviLaser: 8th International Conference on LiDAR Applications in Forest Assessment and Inventory, R.A. Hill,J. Rosette. and J. Sua´rez (Eds), September 2008, Edinburgh, UK, pp. 446–455.
Solberg, S. (2010),”Mapping gap fraction, LAI and defoliation using various ALS penetration variables.” International Journal of Remote Sensing, 31(5): 1227-1244.
Wagner, W., A. Ullrich, V. Ducic, T. Melzer and N. Studnicka, (2006)” Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner” ISPRS Journal of Photogrammetry and Remote Sensing 60(2), pp. 100-112.
Zhao Kaiguang, Sorin Popescu (2009) “Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA” Remote Sensing of Environment 113: 1628–1645